As we step into 2026, the biopharma industry finds itself at a different starting line than anyone predicted just a year ago. As Director of Content & Communication at TriNetX, I’ve watched which resources captivated our community in 2025, and more importantly, why they mattered.
The transformation was unmistakable. Real-world data (RWD) stopped being a buzzword and became the bridge between what we hope therapies can do and what they actually accomplish beyond the controlled conditions clinical trials are designed to create. Trial complexity hit new peaks. Competitive pressures intensified. And as the industry embraced artificial intelligence (AI) and RWD at an unprecedented scale, something essential shifted.
The debate around whether RWD deserved a seat at the table ended. The industry started building the entire table around it.
That shift showed up most clearly in the resources our community couldn’t get enough of. Our rare disease research impact report, the biotech playbook based on a survey of pharma and biotech leaders, and our AI-for-healthcare eBook each reflected a question the industry was urgently trying to answer and captured the progress being made.
These resources rose above because each illuminated a different dimension of the same inflection point. They showed how to solve what traditional trials can’t, how to turn AI and RWD ambition into operational reality, and how to apply these tools responsibly and at scale without sacrificing scientific rigor.
Let’s start with the hardest problem in drug development. Diseases so rare that traditional clinical trials can’t even get off the ground.
RWD (A Cornerstone for Rare Disease Research)
Rare diseases aren’t rare. Between 263 and 446 million people worldwide live with rare diseases at any given time.
Our CSO Perspectives: Research Impact Report—Unlocking Rare Disease Insights became one of 2025’s most downloaded resources because it addressed a truth the industry couldn’t ignore anymore. Traditional clinical trial models are fundamentally broken for rare disease research. When you’re studying conditions affecting thousands (or sometimes just hundreds of patients) globally, you can’t recruit hundreds for a Phase III trial using conventional methods.
The report showcased how companies are solving this seemingly impossible equation. Take Paroxysmal Nocturnal Hemoglobinuria, an ultra-rare blood disorder. Standard studies would have taken years and struggled to find enough patients. Instead, researchers used RWD from the TriNetX LIVE™ platform to reveal something critical. Patients on C5 inhibitors were still experiencing persistent disease activity. That insight didn’t just fill an evidence gap. It provided the rationale for developing an entirely new generation of C3 inhibitors.
This is the power shift happening right now. As Dr. Jeffrey Brown, TriNetX’s Chief Scientific Officer, noted, “Rare diseases are chronically underserved but collectively common. Our data show that when applied thoughtfully, real-world evidence can do more than fill gaps. It can guide entirely new approaches to treatment and care.”
The report resonated because it proved regulatory bodies aren’t just accepting RWD anymore. Regulators are increasingly embracing it, particularly where traditional trials fall short. For an industry watching 7,000 distinct rare conditions remain largely untreated, this represents a critical unlock.
The Biotech Playbook (Industry Leaders Share What They’re Really Thinking)
If rare disease work demonstrated what’s possible with RWD, our Biotech Playbook for Progress revealed what industry leaders are actually planning to do about it.
Drawn from a 2025 survey of 150 senior pharma and biotech executives, the resource became essential reading not because it revealed shocking secrets, but because it quantified what everyone was experiencing: the gap between ambition and execution in the AI and RWD revolution.
The numbers tell a compelling story. 77% of organizations now use RWD in at least some drug development tasks, over half have paired AI with RWD for faster insights, 93% believe AI technologies can make RWD more accessible and impactful, and everyone (yes, 100%) agreed that RWE can improve regulatory submissions.
But here’s where it gets interesting. Despite this overwhelming confidence, the survey revealed significant friction points. Regulatory complexity topped the list of barriers at 36%, with accessibility challenges right behind at 34%. The industry knows where it needs to go. The question is how to get there faster.
What made this resource so engaging was its honesty about the challenges. Steve Kundrot, TriNetX’s Chief Operating Officer, captured it perfectly when he said, “Real-world data is no longer a concept, it’s a capability. The leaders we surveyed see its value and are investing in execution. But to fully realize real-world data’s promise, we must tackle integration challenges, enforce data standards, and build trust in AI applications.”
The report also spotlighted another critical priority. 84% of respondents reported ramping up efforts to improve representativeness and accessibility in clinical research, with nearly all (99%) planning to sustain or expand those efforts. The momentum behind this shift signals a fundamental change in how the industry approaches trial design and patient recruitment.
The AI Wake-Up Call (From Hype to Healthcare Impact)
The rare disease research impact report showed what’s possible. The biotech playbook revealed what executives are thinking. Our AI eBook provided the technical roadmap for actually making it happen.
Artificial Intelligence for the Good of Healthcare: Opportunities for Responsible AI in Clinical Research & Drug Development struck a chord because it moved past AI hype to focus on concrete applications solving real problems right now. Consider these examples.
Automating Protocol Inclusivity. Working with a large pharmaceutical company, TriNetX developed a machine learning (ML) model that analyzes different combinations of trial criteria to optimize representativeness without compromising scientific integrity. As regulators increase documentation requirements, this data-driven approach helps companies meet inclusion enrollment targets while maintaining rigorous scientific standards.
AI-Based Cancer Screening. In a groundbreaking project utilizing TriNetX RWD, researchers analyzed 35,000 pancreatic cancer patients and 1.5 million controls across 55 health systems to identify individuals at risk who are overlooked by traditional screening guidelines. The model identified 87 predictive features and is now being deployed across 6 million patients for prospective monitoring. Early detection can lead to survival rates of over 80% in five years.
Predicting Disease Progression. When a pharmaceutical company needed to understand how follicular lymphoma patients progress between treatment lines, TriNetX used ML on large de-identified electronic health record (EHR) datasets to identify which labs, abnormalities, and treatment pathways correlated with progression. The resulting scoring algorithm enables precise patient selection for trials.
The AI eBook gained traction because it addressed both the opportunity and the responsibility. With the cost of bringing a new drug to market estimated at $314 million-$4.46 billion and 85% of clinical trials facing delays, the pressure to accelerate development is immense. But as the resource emphasizes, the goal isn’t just speed; it’s responsible innovation that improves patient outcomes while maintaining scientific rigor.
What This Means for 2026
Looking across these three resources, a clear narrative emerges.
Data is the new infrastructure. RWD is now as foundational to modern drug development as cloud computing is to modern tech.
Rare diseases are the proving ground. The methods that work for the hardest problems (data harmonization, creative study design, RWE generation) are becoming the blueprint for all therapeutic areas.
Inclusion is table stakes. Representativeness has firmly shifted from moral imperative to operational requirement.
Speed and quality are no longer in tension. AI and RWD are redefining efficiency not as shortcuts, but as smarter, more predictive pathways to scientific rigor.
The transformation is already underway. Leading companies aren’t asking if AI and RWD will reshape development. They’re asking how fast they can scale the capabilities to get better treatments to patients sooner.
Explore these insights further:
CSO Perspectives: Research Impact Report—Unlocking Rare Disease Insights
See how researchers are using RWD to solve the impossible recruitment equation for ultra-rare conditions and why regulators are increasingly building their frameworks around these approaches.
The Biotech Playbook for Progress
Find out where 150 pharma and biotech executives are placing their bets on AI and RWD, what’s slowing them down, and why 99% are doubling down on representativeness despite the obstacles.
Artificial Intelligence for the Good of Healthcare
Go beyond the hype to see the ML models already deployed across millions of patients, from detecting pancreatic cancer earlier to predicting disease progression with precision that traditional methods can’t match.